# Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture

arXiv: https://arxiv.org/abs/2204.00619 · HTML: https://mlsanity.com/paper/2204.00619/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Exploration by Random Network Distillation](https://mlsanity.com/paper/1810.12894/) (2018)
- [The Neural Coding Framework for Learning Generative Models](https://mlsanity.com/paper/2012.03405/) (2020)
- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://mlsanity.com/paper/2012.08621/) (2020)
- [Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations](https://mlsanity.com/paper/1810.07411/) (2018)
- [Gym-μRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning](https://mlsanity.com/paper/2105.13807/) (2021)
- [Backprop-Free Reinforcement Learning with Active Neural Generative Coding](https://mlsanity.com/paper/2107.07046/) (2021)

## Built on by (most cited first, 4 of 4)

- [Brain-inspired Computational Intelligence via Predictive Coding](https://mlsanity.com/paper/2308.07870/) (2023)
- [Are Biological Systems More Intelligent Than Artificial Intelligence?](https://mlsanity.com/paper/2405.02325/) (2024)
- [A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization](https://mlsanity.com/paper/2310.15177/) (2023)
- [Hey Pentti, We Did It!: A Fully Vector-Symbolic Lisp](https://mlsanity.com/paper/2510.17889/) (2025)

## Abstract (arXiv)

We present the COGnitive Neural GENerative system (CogNGen), a cognitive architecture that combines two neurobiologically-plausible, computational models: predictive processing and hyperdimensional/vector-symbolic models. We draw inspiration from architectures such as ACT-R and Spaun/Nengo. CogNGen is in broad agreement with these, providing a level of detail between ACT-R's high-level symbolic description of human cognition and Spaun's low-level neurobiological description, furthermore creating the groundwork for designing agents that learn continually from diverse tasks and model human performance at larger scales than what is possible with current systems. We test CogNGen on four maze-learning tasks, including those that test memory and planning, and find that CogNGen matches performance of deep reinforcement learning models and exceeds on a task designed to test memory.